Collaborative filtering recommendation model based on effective dimension reduction and K-means clustering
Minqiang Li · Jisuanji yingyong yanjiu · 2009
To address the curse of dimensionality,this paper proposed a new hybrid recommendation model which imposed principal components analysis technique combined with K-means clustering.In the approach,the clusters generated from the relatively low dimension vector space transformed by PCA step,and then used for neighborhood selection in order to alternate the exiting K-nearest neighbor searching in high dimensions.The experiment results indicate that the proposed model can produce better prediction quality and higher efficiency.Especially,when the target visitor with few historic information comes,it performs more robust.